Anal Bioanal Chem DOI 10.1007/s00216-015-8575-8

RESEARCH PAPER

Classifying bivalve larvae using shell pigments identified by Raman spectroscopy Christine M. Thompson & Elizabeth W. North & Victor S. Kennedy & Sheri N. White

Received: 26 November 2014 / Revised: 15 February 2015 / Accepted: 17 February 2015 # Springer-Verlag Berlin Heidelberg 2015

Abstract Because bivalve larvae are difficult to identify using morphology alone, the use of Raman spectra to distinguish species could aid classification of larvae collected from the field. Raman spectra from shells of bivalve larvae exhibit bands that correspond to polyene pigments. This study determined if the types of shell pigments observed in different species could be unique enough to differentiate larvae using chemotaxonomic methods and cluster analysis. We collected Raman spectra at three wavelengths from 25 samples of bivalve larvae representing 16 species and four taxonomic orders. Grouping spectra within general categories based on order/family relationships successfully classified larvae with cross-validation accuracies ≥92 % for at least one wavelength or for all wavelengths combined. Classifications to species were more difficult, but cross-validation accuracies above 86 % were observed for 7 out of 14 species when tested using species groups within orders/families at 785 nm. The accuracy of the approach likely depends on the composition of species in a sample and the species of interest. For example, high classification accuracies (85–98 %) for distinguishing spectra from Crassostrea virginica larvae were achieved with a set of bivalve larvae occurring in the Choptank River in the Chesapeake Bay, USA, whereas as lower accuracies (70–92 %) were found for a set of C. virginica larvae endemic to the Northeast, USA. In certain systems, use of Raman spectra appears to be a promising method for assessing the presence of certain bivalves in field samples and for validating highthroughput image analysis systems for larval bivalve studies. C. M. Thompson (*) : E. W. North : V. S. Kennedy Horn Point Laboratory, University of Maryland Center for Environmental Science, Cambridge, MD 21613, USA e-mail: [email protected] S. N. White Applied Ocean Physics and Engineering Department, Woods Hole Oceanographic Institution, Woods Hole, MA 02543, USA

Keywords Raman spectroscopy . Polyenes . Bivalve larvae . Chemotaxonomy . Classification

Introduction Pigments in calcium carbonate shells of adult bivalves and gastropods and in pearls have been studied using Raman spectroscopy, but relatively less is known about the types and roles of pigments in larval bivalve shells. In adults, coloration can be visible on the shell, often in banding patterns [1], but in larval shells, although these pigments exist [2], they appear to have no role in coloration. Recently, an investigation of the types, distribution, and possible origins of bivalve larval shell pigments indicate that their presence may be specific to certain taxa and perhaps provide clues to the evolutionary history of some of these organisms [2]. The objective of this study was to determine if pigments in larval shells discernable with Raman spectroscopy could be used to identify the taxonomic group of the larvae. Although the presence of pigments in mollusc shells has been noted [3], their chemical composition and location within the shell matrix remained unknown until studies were performed using Raman spectroscopy. Raman spectroscopy is a non-destructive technique that creates a spectrum from light scattering off vibrational energy in organic molecules. The lasers cause a resonance Raman effect to occur with carotenoid-like pigments, allowing their signals (reported as inverse centimeters, cm−1) to be viewed strongly at laser wavelengths in the 400–800-nm ranges. Studies on bivalves with Raman spectroscopy have revealed carotenoid-like pigments (polyenes) with conjugated single and double carbon bonds [1, 4]. Unlike regular carotenoids, these molecules are un-substituted and lack side methyl groups [1, 4, 5]. The bands with highest intensity in Raman spectra correspond to C=C (ν1) and C–C (ν2) stretching modes that typically occur

C.M. Thompson et al.

between 1450 and 1680 and 1070 and 1210 cm−1, respectively. Other, smaller, bands are the ν3 CH=CH in-plane rocking mode from 1295 to 1300 cm−1 and the ν4 CH=CH out-ofplane wagging mode at 1000–1015 cm−1 [1]. The wavenumber shifts of ν1 and ν2 correspond to the number of carbon– carbon double bonds, and this number can be determined by a linear relationship [1]. Most pigment chain lengths in molluscs range from 9 to 13 conjugated double bonds but could be as high as 29 [1, 4]. More recent evidence in pearl studies have revealed that these pigments likely occur in multiple combinations and orientations [2, 6], which can be detected by resonance effects with different lasers and by decomposing spectra. Different matrix organization or structure between species could result in different pigments forming or different orientations of pigments occurring, which would be reflected in the Raman spectra. Comparisons between intact and decalcified adult bivalve shells using Raman spectroscopy have indicated that shell pigments form complexes with the calcium carbonate matrix that forms the shell, most likely integrated with the organic portion of the matrix [1, 6]. Adult shell microstructures have revealed different organic matrix organizations at the superfamily level as a result of biological and environmental factors [7]. In addition, pearls of the same color from different mollusc families were shown to be composed of different mixtures of pigments and that molecule orientations and bent chain interlacing with the shell matrix can lead to shifts in spectra [8]. It is unknown whether the types of pigments present in adult mollusc shells are indicative of shell color, taxa, or environmental conditions. Regions of different color on a shell produce different wavenumber shifts for ν1 and ν2 peaks by as much as 12 cm−1 [1, 4]; however, this does not necessarily explain all the diversity in shell color. Studies also have shown increased presence of polyenes in growth lines [9], as well as lack of pigment in some white pearls and shells [4, 10]. The presence of different chain lengths, chemical combinations, orientations, and relative proportions of pigments all may contribute to coloration [6], as two shells with the same color may show different polyenes in their Raman spectra. It is unknown how pigments are synthesized in molluscs, but it is likely that they are modified from pigments received in an algal diet [10]. Recent work has found the same polyene pigments in larval bivalves that are seen in adult bivalve shells [2]. In larvae, these pigments were not associated with visible coloration or growth lines. As with adults, different species of larvae showed different wavenumber shifts for pigments at 532 nm. These differences seemed to be associated with genera: scallops, clams, mussels, and oysters revealed distinct spectral differences in the ν1 and ν2 regions. Within species, slight wavenumber shifts of ν1 and ν2 were observed in the early part of the larval shell, the prodissoconch I, but these shifts were not generally large enough to indicate different

chain lengths of polyenes. Pigments may be important for larval shell development because they are incorporated into the organic matrix even before the larva starts feeding and may stabilize crystallization [2]. The differences observed in Raman signatures in larval shells could help identify bivalve larvae, which are notoriously difficult to identify. The best methods are either limited by cost, accuracy, or start-up time [11, 12]. Using Raman spectroscopy to identify larval bivalve shells could augment some of these technologies if it has high accuracy [2]. This would significantly aid the ability to track spawning and larval dispersal in the field and augment marine spatial planning and restoration activities. Raman spectra have been used in other studies to identify microbial cells and fungi [13–16], and we extended this chemotaxonomic approach to bivalve larvae in this study. The objective of the study was to determine if Raman signatures at three wavelengths (532, 638, 785 nm) could be used to distinguish bivalve larvae. We tested (1) whether pigments in Raman spectra could be grouped based on species, order, or family, (2) if Raman spectra could be used to classify closely related species within an order, and (3) if Raman spectra could be applied to identify species of bivalves collected within geographic regions.

Methods To determine if Raman spectra could be applied to identify bivalve larvae, we assembled 25 samples of bivalve larvae representing different age classes from 16 species either spawned in our laboratory or acquired from hatcheries. Raman spectra were taken from 40 to 60 shells in each sample representing equal numbers of early- (d-stage), middle- (veliger), and late-stage (pediveliger) larvae. After standardizing the spectra, cross-validation and classification analyses were performed to determine whether spectra could be used to distinguish individuals between orders/family groups, between species within orders, and between species within geographic regions. Sample acquisition Bivalve larvae for this project were acquired with three methods: using available larvae that had been previously spawned and preserved in the laboratory [17], acquiring adult individuals and spawning them in the laboratory, and acquiring samples from other hatcheries. For laboratory spawning, ripe adult broodstock of Mulinia lateralis (family: Mactridae), Macoma mitchelli (family: Tellinidae), Gemma gemma (family: Veneridae), and Mytilopsis leucophaeata (family: Dreissenidae) were collected from the Choptank River in June 2012 (Table 1). G. gemma

Order (family)

Pterioida (Pectinidae) Pterioida (Pectinidae) Pterioida (Pectinidae) Pterioida (Ostreidae)

Pterioida (Ostreidae) Pterioida (Ostreidae) Pterioida (Ostreidae) Pterioida (Ostreidae) Veneroida (Veneridae) Mytiloida (Mytilidae) Mytiloida (Mytilidae) Veneroida (Tellinidae) Veneroida (Veneridae) Veneroida (Veneridae) Veneroida (Veneridae) Veneroida (Veneridae) Veneroida (Mactridae) Veneroida (Mactridae) Myoida (Myidae) Veneroida (Dreissenidae) Pterioida (Ostreidae) Myoida (Hiatellidae)

Veneroida (Mactridae) Veneroida (Mactridae) Veneroida (Solecurtidae)

Species

Argopecten irradians A. irradiansa A. irradians Crassostrea gigasa

C. gigas Crassostrea virginica C. virginicaa C. virginica Gemma gemma Geukensia demissa Ischadium recurvum Macoma mitchelli Mercenaria mercenariaa M. mercenaria M. mercenaria M. mercenaria Mulinia lateralis M. lateralisa Mya arenaria Mytilopsis leucophaeata Ostrea lurida Panopea generosa

Rangia cuneata Spisula solidissma Tagelus plebeius

Cambridge, MD Woods Hole, MA Cambridge, MD

Hilo, HI Dennis, MA Cambridge, MD Gloucester Point, VA Cambridge, MD Cape May, NJ Cambridge, MD Cambridge, MD Gloucester Point, VA Dennis, MA Dennis, MA Machias, ME Cambridge, MD Cambridge, MD Machias, ME Cambridge, MD Ti lamook, OR Seward, AK

Dennis, MA Dennis, MA Woods Hole, MA Ti lamook, OR

Location

2009 2012 2009

2012 2010 2011 2009 2009 2012 2009 2012 2009 2009 2010 2009 2009 2012 2009 2012 2012 2013

2008 2010 2012 2012

Year

66 20 56

66 66 66 66 22 66 66 64 66 22 44 66 44 64 66 64 66 44

44 66 44 66

No. of spectra

1130 (1) 1129 (3) 1129 (2)

– – – – 1128 1105 (1) 1105 (1) 1129 (2) 1130 (1) 1130 (1) 1130.5 (1) 1130 (1) 1128 (1) 1128 (2) – – – 1128 (1)

– – – – – 1012 1013 1015 1012 1018 1011 1013 1007 1012 – 995 – 1020 1014 1018 1010

1126 (1) 1124 (2) 1123 (2) –

1010 1011 1013 –

1517 (3) 1517 (4) 1517 (4)

– – – – 1514 1480 (6) 1480 (3) 1517 (2) 1521 (1) 1516 (3) 1521 (1) 1519 (2) 1514 (8) 1517 (4) – – – 1514 (1)

1514 (2) 1510 (3) 1507 (2) –

1014 1015 1009

– – – – – 1013 1014 1013 1011 1015 1010 1012 1021 1012 – 1006 – 1119

1005 1013 1012 –

ν4

1120 (1) 1121 (2) 1119 (1)

– – – – 1120 1099 (1) 1097 (0.1) 1119 (1) 1120 (1) 1122 (1) 1121 (1) 1121 (1) 1120 (4) 1118 (1) – – – 1125 (1)

1118 (2) 1116 (1) 1114 (1) –

ν2

ν1

ν4

ν2

638 nm

532 nm

1503 (1) 1504 (3) 1502 (1)

– – – – 1504 (5) 1483 (4) 1481 (1) 1503 (1) 1502 (1) 1504 (1) 1503 (1) 1503 (1) 1503 (10) 1501 (1) – – – 1508 (1)

1500 (3) 1498 (1) 1497 (1) –

ν1

– – – – – – – 1122 (4) 1116 (1) 1119 (3) 1119 (2) 1116 (2) 1113 (6) 1113 (5) – – – 1125 (1) 1114 (2) 1115 (5) 1114 (3)

– – –

1112 (2) 1103 (3) 1103 (4) –

ν2

– – – – – – – – – – 1007 1006 – – – – – –

1004 – 1014 –

ν4

785 nm

1493 (11) 1501 (20) 1496 (22)

– – – – – 1470 (4) 1471 (18) 1503 (18) 1496 (1) 1499 (2) 1499 (2) 1497 (2) 1497 (26) 1492 (26) – – – 1506 (1)

1492 (1) 1489 (2) 1488 (10) –

ν1

Table 1 Information about each larval sample and average wavenumber peaks (cm−1) in pigment regions of the spectra. Location of spawning and year spawned is shown for each sample, species, order, and family, as well as the number of spectra used to find the average wavenumber peak (cm−1) for each pigment region (ν4, ν2, and ν1) for each wavelength (532, 538, 785 nm). One standard deviation (cm−1) is presented for the strongest peaks, ν1 and ν2. The B–^ indicates that there was no pigment peak present. a Indicates which samples were used in cross-validation analyses (Tables 2 and 3) and in Fig. 2 when multiple samples were obtained from different locations or years

Classifying bivalve larvae with Raman spectroscopy

C.M. Thompson et al.

bears live juveniles, which were expelled into dishes holding the adults, collected, and preserved in ethanol. Larvae of the other three species were spawned in the laboratory from June to August by subjecting adults to alternating hot and cold temperatures to induce spawning. Fertilized eggs were placed in 3 L glass containers at starting densities of 23–30 eggs mL−1. After 24, 48, and every 48 h thereafter, approximately 200 larvae were sampled and preserved in ethanol. After 24 h, larvae were fed a mixture of Isochrysis galbana (strain C-ISO) and Thalassiosira pseudonana (strain 3H) at a density of 5×105 cells mL−1. Other species were acquired from laboratories or hatcheries from 2008 to 2013 (Table 1). Although spawning, rearing, and preservation protocols differed by location, samples of 500– 2000 larvae were collected at three developmental stages during culture (d-stage, veliger stage, and pediveliger stage) and preserved in ethanol. Samples were stored from a few weeks to years, and only intact shells were used for analysis. To prepare larvae for Raman acquisition, samples were bleached to remove tissue. This step is necessary because the larval shells are translucent, and tissue beneath creates fluorescence that overwhelms the Raman signal. Larvae were first rinsed in deionized water and then treated with a 40 % sodium hypochlorite (bleach) solution for 20 min and lightly shaken. The larvae were rinsed in water again to remove bleach and allowed to air-dry on a quartz slide. This procedure did not affect pigment spectra or incorporate Raman bands from bleach or water into the spectra [2].

Raman acquisition Raman spectra were acquired with an XploRA confocal Raman microscope by Horiba Jobin Yvon, Inc. The system included a flat field spectrograph with a multichannel air-cooled CCD detector and color camera optically coupled to an Olympus BX41 microscope. We used three lasers: a 532-nm 25mW solid-state laser, a 638-nm 25-mW laser diode, and a 785nm 25-mW laser diode. The filter was set for 25, 50, and 100 % for each laser, respectively. The lasers ran through a ×100 objective using a 1200-g mm−1 grating and hole size of 100 μm and slit size of 300 μm. Spectra were recorded in the range of 200–2000 cm−1. Spectra were acquired from 20 larval shells for each sample by averaging three accumulations with an exposure time of 10 s. Peak intensity of both calcium carbonate and pigments remained the same for each accumulation demonstrating minimal to no damage on the sample by the laser. For one shell in each sample, three spectra were taken from different positions on the shell. Spectral acquisition was controlled using Horiba’s LabSpec software (version 6). Wavelength calibration was performed on the XploRA system using a neon light source that was calibrated daily with a silicon wafer.

Analysis All spectra were first pre-processed to remove noise and other variability. Immediately after acquisition, noise was removed using a smoothing function in LabSpec. Baseline correction was then performed using a freely available integrated software system for processing Raman spectra [18] implemented in MATLAB (v. R2011a). Next, all wavenumbers were shifted to ensure the aragonite peak for all spectra fell at 1085 cm−1, and then spectra were standardized to the intensity of the aragonite peak on a scale of 0 to 1. Descriptive plots of averaged spectra were created to visually compare spectral characteristics between each species and taxonomic group. An average spectrum for each sample was created by averaging the values from individual spectra at each Raman shift. For species whose shells contained pigments, a scatterplot of the average wavenumber peaks corresponding to C–C (ν2) and C=C bonds (ν1) at 532 nm was created to determine if spectral peaks could be used to visually distinguish larval shells by order or Species. To determine whether spectra could be used to distinguish individuals between orders/family groups, between species within orders, and between species within geographic regions, cross-validation and classification analyses were performed on samples of spectra and resulting accuracies were reported as the percentage of spectra correctly assigned to a category. A category was defined as a grouping of either a single species or a mix of different species using one sample per species unless indicated otherwise. A sample consisted of spectra of larvae of a species from a unique location and spawning year. There were multiple samples for several species (i.e., larvae were spawned in different locations or in different years at the same location). Categories of spectra were grouped to create training sets which were used to inform (Btrain^) the computer software of the characteristics of each group. Four training sets were created for each set of analyses using spectra taken at (1) 532 nm, (2) 638 nm, (3) 785 nm, and (4) combined spectra in which spectra from each wavelength were appended to produce one long spectrum. Cross-validation analyses were conducted to assess the accuracy of a training set in classifying individual spectra within the training set using a leave-one-out (LOO) routine within the support vector machine (SVM) classifier [19]. Each training set was run separately through a principal components analysis (PCA) in MATLAB to ensure that the SVM was trained with the spectral data that contained the bulk of the variation between categories. Next, the SVM would go through each spectrum in the training set, each time leaving one out and training with the rest of the spectra. The SVM then used a one-to-one classification routine to match each left-out spectrum to one of the categories in the training set based on the PCA component features.

Classifying bivalve larvae with Raman spectroscopy

Classification analyses also were conducted in which a training set was used to classify the spectra of a separate sample (a sample that was not included in the training set) into a training set category using the SVM. In this type of analysis, each spectrum in the separate sample was compared with the categories in the training sets based on the PCA component features. Greater than 80 % accuracy was considered a successful classification. To determine if larvae could be identified to order/family, spectra were placed into five categories (Btaxa-based^ training sets) based on both their taxonomic order/family and spectral similarities and then a cross-validation analysis was conducted. The categories within the taxa-based training set were (1) clams (with pigments): M. mitchelli, Mercenaria mercenaria, M. lateralis, Panopea generosa, Rangia cuneata, Spisula solidissima, and Tagelus plebeius; (2) mussels: Geukensia demissa and Ischadium recurvum; (3) oysters: Crassostrea gigas, Crassostrea virginica, and Ostrea lurida; (4) scallops: Argopecten irradians; and (5) other bivalves (no pigment): Mya arenaria, M. leucophaeata. Note that scallops and oysters were grouped separately due to their differences in spectra despite both being in the order Pterioida. If multiple samples of one species were present, then one sample was chosen for inclusion in the category (noted in Table 1). For categories containing spectra from multiple species, spectra were selected at random for each wavelength to create approximately equal numbers of spectra from each species to fill the categories evenly (total of n=66 for each category). For this and the subsequent analyses, G. gemma was not included because (1) the larvae from this brooding species were large and are not likely to be found in plankton samples, (2) they have a distinctive shell and are easy to distinguish from other species, and (3) approximately half of the specimens had pigments whereas the other half did not, thus making it difficult to group into one of the above categories. To test if species within a taxa-based category could be distinguished from each other, four separate cross-validation tests were conducted for (1) clams with pigments, (2) mussels, (3) oysters, and (4) other bivalves (no pigment) using categories comprised of spectra of individual species (n=66) within these three groups. The same samples that were employed in the order-level analyses were used in these species-level analyses. To test if species that were found within a geographic region could be distinguished from each other, cross-validation tests were performed using categories of species based on region. The species categories were grouped in three Bregional^ training sets: Choptank River, Northeast, and West coast groups. Eight species from the Choptank River were grouped together to represent species that spawn in the spring and summer months in the Choptank River estuary in the Chesapeake Bay (USA). These species were C. virginica, G. demissa, I. recurvum, M. mitchelli, M. lateralis,

M. leucophaeata, R. cuneata, and T. plebeius. Six species were chosen to represent species found in the Northeast from hatcheries in Cape Cod and Maine (USA), including A. irradians, C. virginica, G. demissa, M. mercenaria, M. arenaria, and S. solidissima. Similarly, the three West coast (USA) species were grouped together: C. gigas, O. lurida, and P. generosa. Cross-validation analyses were conducted with each of these regional training sets. In addition, classification analyses were conducted using the taxa-based and regional training sets to determine if the training sets could be used to identify spectra that were from samples which were not included in training sets (i.e., separate samples). Essentially, the spectra from the separate samples were treated as if they were from unknown specimens and the SVM was used to classify them into the categories within each training set. The classification analyses were conducted to assess how well Raman spectroscopy performed when used to distinguish larvae from different locations or cultures which were not represented in the training sets. Finally, a decision-tree diagram was made that summarized the salient information from spectra of the 16 species which would enable unknown spectra to be classified into taxonomic categories by visual inspection (i.e., without an SVM). This diagram was tested using the nine separate samples that were the subject of classification analyses using the SVM.

Results Distinguishing spectra between order/family The first objective of this research was to test whether spectra of species from the same or similar taxonomic orders or families would present similar spectra that may reflect evolutionary similarities in shell formation. The sample set contained 16 species from four different taxonomic orders. Table 1 shows the average wavenumber shifts of each sample for each pigment region at each of the three wavelengths. Standard deviations for wavenumber peaks for ν2 and ν1 were generally low (between 1 and 3 cm−1) at the 532- and 638-nm wavelength for all species but were as high as 18–26 cm−1 for some peaks of clam spectra at 785 nm (Table 1). Five species did not show any peaks corresponding to pigment material for any laser wavelength. These included three oysters (C. virginica, C. gigas, and O. lurida), one clam (M. arenaria), and one dreissenid mussel (M. leucophaeata). The 11 samples that had pigments corresponding to polyenes were plotted to visualize their relationships based on the wavenumbers of the C–C (ν2) and C=C bonds (ν1) at 532 nm where the peaks were strongest (Fig. 1). The wavenumber shifts for the carotenoid beta carotene are shown for comparison [4]. This figure shows how pigment wavenumbers cluster with respect to the different orders. This plotting method alone was sufficient for distinguishing one order (Mytiloida, mussels, species 5 and

C.M. Thompson et al. 1530 Pterioida Mytiloida Veneroida Myoida Carotenoid

1520 ν1 (C=C), Wavenumber/cm −1

Fig. 1 Scatterplot of wavenumbers for all species and samples presenting polyenic pigments in their spectra. Samples are grouped by order (different symbols) with numbers corresponding to species names next to each point. Average wavenumbers are for all spectra in the sample at 532 nm (for number of spectra, see Table 1) and standard deviations for both ν1 and ν2 are plotted for each sample. Beta carotene wavenumber shifts (purple diamond) are shown for comparison with carotenoid pigments

8,10 11 7, 13-16 9 4, 12, 17

1

18

2

1510

3 1 - Argopecten irradians, Dennis, MA 2008 2 - A. irradians, Dennis, MA 2010 3 - A. irradians, Woods Hole, MA 2012 4 - Gemma gemma, Cambridge, MD 2009 5 - Geukensia demissa, Cape May, NJ 2012 6 - Ischadium recurvum, Cambridge, MD 2009 7 - Macoma mitchelli, Cambridge, MD 2012 8 - Mercenaria mercenaria, Gloucester Point, VA 2009 9 - M. mercenaria, Dennis, MA 2009 10 - M. mercenaria, Dennis, MA 2010 11 - M. mercenaria, Machias, ME 2009 12 - Mulinia lateralis, Cambrdige, MD 2009 13 - M. lateralis, Cambridge, MD 2012 14 - Rangia cuneata, Cambridge, MD 2009 15 - Spisula solidissma, Woods Hole, MA 2012 16 - Tagelus plebeius, Cambridge, MD 2009 17 - Panopea generosa, Seward, AK 2013 18 - Beta carotene (Hedegaard et al. 2005)

1500

1490

5, 6

1480

1470 1100

1110

1120

1130

1140

ν2 (C−C), Wavenumber/cm

6 on Fig. 1) from the other three orders (Myoida, Pteroida, and Veneroida). The wavenumbers of the species in the Veneroida and Myoida (clams) closely overlapped. Average spectra grouped by order for the 532-nm laser also reveal that species within a taxonomic order did not always have similar spectra (Fig. 2). The Mytiloida (mussels) was the only order that showed strong similarities within the group. Fig. 2 Average spectra for representative samples from each species at the 532-nm wavelength and standardized to the aragonite peak at 1085 cm−1. The number of spectra for each sample is presented in Table 1 (see asterisk in cases with multiple samples per species). Species are grouped by order and family. Starred species in the figure represent those that are brooders (i.e., retain their larvae internally for a period of time before release)

1150

1160

−1

The Pteroida group contained both the bay scallop and the three oysters. All three oysters had similar spectra and did not present any pigment shifts in the ν1 (1100–1130 cm−1) or ν2 regions (1480–1520 cm −1 ). Similarly, the clam (M. arenaria) and the dreissenid (M. leucophaeata) did not present pigments whereas other species within those orders had clear polyene signatures.

Pterioida - pectinidae

Argopecten irradians Crassostrea gigas

Pterioida - ostreidae

Crassostrea virginica Ostrea lurida* Geukensia demissa

Mytiloida - mytilidae

Intensity

Ischadium recurvum Mya arenaria

Myoida - myidae

Panopea generosa

Myoida - hiatellidae

Mytilopsis leucophaeata

Veneroida - dreissenidae

Mercenaria mercenaria

Veneroida - veneridae

Gemma gemma* Veneroida - mactridae

Rangia cuneata Spisula solidissima Mulinia lateralis

Veneroida - tellinidae

Macoma mitchelli

Veneroida - solecurtidae

400

600

Tagelus plebeius

800

1000

1200

1400

Wavenumber/cm

−1

1600

1800

2000

Classifying bivalve larvae with Raman spectroscopy Table 2 Cross-validation accuracies derived from training sets in which samples were grouped based on taxonomy and spectral similarities. These Btaxa-based^ training sets are indicated by a letter and are followed by a list of the categories within the set. Number of spectra and the percentage correctly classified at each wavelength are listed for the overall test (in bold) and for each category. The crossvalidation accuracies were calculated using a leave-one-out routine in a support vector machine. Separate tests were conducted using spectra taken at wavelengths of 532, 638, and 785 nm and using Bcombined^ spectra in which spectra from one larval shell that were taken at each wavelength were appended to produce one long spectrum Taxa-based groupings

No. of 532 nm 638 nm 785 nm Combined spectra (%) (%) (%) (%)

Pooled categories 330 Clams (with 66 pigment) Mussels 66 Oysters 66 Scal lops 66 Other bivalves (no 66 pigment) Clams (with pigment) 380 Macoma mitchelli 64 Mercenaria 66 mercenaria Mulinia lateralis 64 Panopea generosa 44 Rangia cuneata 66 Spisula solidissma 20 Tagelus plebeius 56 Mussels 132 Geukensia demissa 66 Ischadium recurvum 66 Oysters 176 Crassostrea gigas 66 Crassostrea virginica 66 Ostrea lurida 44 Other bivalves (no 108 pigment) Mya arenaria 42 Mytilopsis 66 leucophaeata

87 89

91 89

76 39

90 92

95 79 88 82

95 83 95 91

89 95 71 86

91 73 95 97

36 17 71

47 8 71

85 86 89

52 52 70

59 68 15 0

67 89 65 0

74 93 88 20

33 86 73 0

0 73 70 76 64 67 67 57 89

4 69 65 73 43 56 58 0 94

91 92 92 92 44 53 64 0 100

18 70 73 67 51 30 97 14 92

94 81

88 98

100 100

86 95

To determine how well larval shells could be distinguished between orders or family, the taxa-based training set was used with the SVM to calculate cross-validation accuracies for classifying spectra into the following categories: (1) clams (with pigments), (2) mussels, (3) oysters, (4) scallops, and (5) other bivalves (no pigment) (Table 2 (pooled categories)). Highest cross-validation accuracies for distinguishing categories were obtained with the 638 nm wavelength for all groups (>83 % accuracy). The combined spectra produced high results for most groups (91–97 % accuracies), but not overall, as the oysters cross-validation was below 80 %. Overall, using

categories based on order/family, relationships enabled classification of spectra with cross-validation accuracies ≥92 % for at least one wavelength or for the combined wavelengths. Distinguishing between species within orders The second goal of this work was to test if species within similar taxonomic groups could be distinguished. Most spectra for mussels and clams were successfully distinguished using the SVM classifier at 785 nm (Table 2 (clams (with pigment) and mussels)). For mussels, the training set consisted of two species, I. recurvum and G. demissa. At 532 nm, spectra for both species were very similar. They presented ν2 peaks at 1105 cm−1 and a combination of at least two ν2 peaks at 1480 cm−1 (Fig. 2; Table 1). At 638 nm, the ν2 peak was shifted up, and at 785 nm, the ν2 peak was very weak. The SVM had 92 % accuracy when distinguishing the two mussel species at the 785 nm wavelength; however, no visual characteristics in the spectra can be identified to make this distinction without help from a classification routine. For the seven species of clam that had pigments in their spectra, the SVM was able to distinguish 5 out of 7 with ≥86 % accuracy at 785 nm (Fig. 3; Table 2 (clams (with pigment)). P. generosa had noticeably different pigments based on wavenumber (and is the only Myoida clam represented) (Fig. 3; Table 1) whereas the other species differed in peak intensity at this wavelength (Fig. 3). S. solidissima had a weak spectrum overall, but there were fewer spectra available for this sample (Table 1). Spectra of the three species of oysters were unable to be distinguished using the SVM or visual methods (Table 2 (oysters)); however, the other bivalves not showing pigments were distinguished successfully at all wavelengths (Table 2 (other bivalves (no pigment))). For oysters, no test with any wavelength had greater than 80 % accuracy for more than one species. However, we found that Raman can be used to distinguish oysters from the spectra of other species that do not contain pigments. At the 638- and 785-nm wavelengths, oysters exhibited unique spectra due to the fact that the intensity correction settings on the spectrometer were not applied (Fig. 4). This causes spectra from shells that are very thin to exhibit Bbumps^ of varying intensity, particularly in the smaller stages. Although mostly noise, these intensity variations can still be useful for classification. At 638 nm, 96 % of C. gigas, 83 % of C. virginica, and 95 % of O. lurida showed this pattern. In contrast, 50 % of M. leucophaeata and 0 % of M. arenaria had this effect. At 785 nm, 97 % of C. gigas, 81 % of C. virginica, and 91 % of O. lurida showed large bumps, but no spectra from the other species showed them. The C. virginica larvae that did not have bumps were mostly pediveliger larvae with thicker shells. Thus it may be possible to identify species without pigments at these wavelengths using intensity variations, despite it not being useful for SVM classification.

C.M. Thompson et al. Fig. 3 Clam spectra at 785 nm. Average spectra for each sample standardized to the aragonite peak at 1085 cm−1. The number of spectra can be cross-referenced in Table 1 for each species (see asterisks for species with multiple samples) Intensity

Panopea generosa Mercenaria mercenaria

Rangia cuneata Spisula solidissima Macoma mitchelli Mulinia lateralis Tagelus plebeius

400

600

800

1000

1200

1400

1600

1800

2000

−1

Wavenumber/cm

Distinguishing between species within geographic regions Cross-validation analyses were conducted with regional training sets to determine how well individuals were distinguished from species found in the same geographic region (Choptank River, Northeast, West coast). The Choptank River training set received at best 77 % overall accuracy when using the spectra from a 785-nm laser (Table 3 (Choptank River)). The Northeast training set had 95 % overall accuracy using combined spectra from all three wavelengths (Table 3 (Northeast)). Because this training set included hatcheryreared larvae for the most part, this set was not exhaustive of species in this area or representative of any particular estuary. The best cross-validation accuracy achieved for the West coast training set was 72 % using a 532-nm laser (Table 3 (West coast)). Using Raman spectra to identify larvae reared at different locations or times Classification analyses were conducted with separate samples to determine if Raman spectra could be used to identify individuals that were reared in conditions that were different from the training sets. Nine separate samples from five species were classified using the taxa-based training set, the regional training sets, and taxa-based regional training sets, the latter of which was comprised of taxa-based categories that included only species from each region. The taxa-based regional training sets generally had higher classification accuracies than the taxa-based and regional training sets (Table 4). For A. irradians, the Northeast taxa-based training set had accuracies >80 % for both samples: 91 % (sample from Dennis, MA at 638 nm) and 82 % (sample from Woods Hole, MA at 638 nm). Spectra of M. lateralis were

distinguished with highest accuracy (95 %) using the Choptank taxa-based training set on the combined spectra. For M. mercenaria, both taxa-based and Northeast taxabased training sets had 100 % accuracy using the combined spectra. For C. gigas, both the West coast and West coast taxabased training sets were able to classify 100 % of the spectra as C. gigas at 638 nm. The highest accuracies were achieved for C. virginica with the Choptank River training set at 785 nm (85 and 98 % for the two samples), but Northeast training set failed to classify one sample with accuracy of >80 %. In contrast, the Northeast taxa-based training set was able to classify the two samples (82 and 100 % accuracies), whereas accuracies with the Choptank taxa-based training set were 73 and 97 %. A decision-tree diagram was created to enable manual classification of spectra to order/family without an SVM based on key characteristics observed within each taxabased group (Fig. 5). First, the presence of pigments at 532 nm was used to separate larval shells with pigments from those without. For bivalves that did not have pigments, shell thickness signatures at 638 or 785 nm then were used to distinguish oysters from other bivalves. For the clam, scallop, and mussel larvae with shell pigments, wavenumbers of ν1 and ν2 at 532 nm were used to make the classifications to order/family. To test the decision tree, separate samples were classified into the taxa-based training set categories, which resulted in accuracies from 74 to 100 % (Table 5), which were equal to or higher than those with the SVM for six of the nine samples (compare Table 5 with Table 4 (taxa-based)). Misclassifications occurred for some spectra where the decisions were

Classifying bivalve larvae with Raman spectroscopy

a

Mytilopsis leucophaeata

Intensity

Fig. 4 Average spectra of four species that do not show pigments at a 638 and b 785 nm. Average spectra for each sample standardized to the aragonite peak at 1085 cm−1. The number of spectra can be cross-referenced in Table 1 for each species (see asterisks for species with multiple samples)

Mya arenaria

Crassostrea virginica

Crassostrea gigas

400

600

800

1000

1200

1400

1600

1800

2000

−1

Wavenumber/cm

b

Intensity

Mytilopsis leucophaeata

Mya arenaria

Crassostrea virginica

Crassostrea gigas

400

600

800

1000

1200

1400

1600

1800

2000

−1

Wavenumber/cm

inconclusive (the cases where spectra had a ν2 shift of greater than 1126 cm −1 but a ν1 shift of less than 1515 cm−1, or spectra of oyster pediveligers that had mostly flat lines at 638 and 785 nm).

Discussion We found that Raman spectra of bivalve larvae are different enough to be distinguished between order/family-based categories as well as between some species within these categories. The use of multiple wavelengths added more variability to the spectra which assisted with distinguishing between groups. The results of the classification analyses suggest that

this technique has promise for identifying bivalve larvae in the wild for certain applications. There were clear differences in spectra that enabled the use of Raman spectra to distinguish bivalve larvae between orders, as has been found in an earlier study on seven species of bivalve larvae [2]. Here, the Mytiloida had the greatest difference in pigment spectra with ν1 and ν2 bands shifted up from the other species. Results of a study on adult bivalve shells showed similar shifts in spectra with Mytilus edulis, another Mytiloid mussel [4]. The scallops in the Pterioida and the clams in Myoida and Veneroida also showed wavenumbers for polyenes that differed between orders and families, but these peaks were more closely clustered (Fig. 1). Scallops may have slightly longer polyene chains than clams based on their wavenumbers. In one Raman study, the adult

C.M. Thompson et al. Table 3 Cross-validation accuracies derived from training sets in which samples were grouped based on geographic region. The Bregional^ training sets are indicated by a letter and are followed by a list of the species categories within the set. Number of spectra and the percentage correctly classified at each wavelength are listed for the overall test (in bold) and for each category. The cross-validation accuracies were calculated using a leave-one-out routine in a support vector machine. Separate tests were conducted using spectra taken at wavelengths of 532, 638, and 785 nm and using Bcombined^ spectra in which spectra from one larval shell that were taken at each wavelength were appended to produce one long spectrum Regional group

No. of spectra

532 nm 638 nm 785 nm Combined (%) (%) (%) (%)

Choptank River 490 Crassostrea 66 virginica Geukensia 66 demissa Ischadium 66 recurvum Macoma 64 mitchelli Mulinia lateralis 64

50 86

58 86

77 100

76 89

62

59

80

73

67

82

92

82

16

48

67

75

30

80

56

59

Mytilopsis 42 leucophaeata Rangia cuneata 66 Tagelus plebeius 56 Northeast 350 Argopecten 66 irradians Crassostrea 66 virginica Geukensia 66 demissa Mercenaria 66 mercenaria Mya arenaria 66 Spisula 20 solidissma West coast 154 Crassostrea 66 gigas Ostrea lurida 44 Panopea 44 generosa

50

86

86

83

64 21 92 97

6 20 92 95

80 54 76 59

86 59 95 95

92

91

94

94

92

97

83

97

98

98

62

100

98 30

100 15

92 40

100 55

72 98

70 98

69 97

65 82

5 100

0 98

0 95

7 98

scallop shells of Chlamys senatoria and Chlamys mauritana revealed spectral bands in the ν1 and ν2 range (1120–1128 and 1501–1511 cm−1 at 514 nm); however, there were much more variations because shells with different colors were studied [4]. Differences in the ν2 bands may reveal different pigments between samples, and a high standard deviation in spectra within clams could indicate intraspecies variation in coloration that may make them difficult to distinguish. Although the difference in spectra between families may reflect evolutionary similarities in shell formation, grouping spectra solely according to taxonomy was not as successful at

distinguishing bivalve larvae as groups based on both taxonomic and spectral similarity. For instance, we split bivalves without pigments from the clams although the orders of the species were similar, and we separated oysters from scallops in the Pterioida. These types of categories rather than strict taxonomic categories may be more useful for classifying bivalve larvae with Raman spectra. Multiple wavelengths gave more options to classify to order/family using resonance effects. Still, peaks became less sharp with increasing wavelengths, making it more difficult to locate an exact wavenumber for each pigment peak and creating more between-shell variation. Nevertheless, this method using multiple wavelengths performed well for our samples, and the 638-nm wavelength performed the best with all groups having greater than 80 % accuracy. At this wavelength, the combination of lower standard deviation in spectral peaks and intensity variations in the non-pigmented spectra provided the best ability to classify each group. It was more difficult to use Raman spectra to distinguish species within the taxa-based categories, although crossvalidation accuracies were >80 % for 9 out of 14 species at 785 nm (Table 2 (clams (with pigment), mussels, oysters, and other bivanlves (no pigment))). For the mussels and the clams with pigments, the wavelength that best separated the species (785 nm) had the worst results for the order/family-based tests (which were best separated at 638 nm). This may be due to variability in the 785-nm spectra that enabled greater separation of the species categories but could have reduced separation between order/family categories. It should be noted that spectra were difficult to distinguish visually for clams and mussels, necessitating classification methods like SVMs. For species that did not show pigments, turning off the spectrometer’s intensity correction created discernible spectral patterns that enabled differentiation of oysters from clams. This method worked best visually because the SVM did not have high accuracy comparing these spectra. There are drawbacks to using the noise produced based on spectral intensity; it is less amenable to comparisons between spectrometers, it relies on shell thickness which could be variable and difficult to standardize, and it may be influenced by temperature or humidity. It is important to note that thin shells are characteristics prominent at the D-stage, and some later stages with thicker shells may not present this pattern as clearly. A useful application of Raman technology would be to identify bivalve larvae in samples collected in the field. The performance of this method may be influenced by the bivalve species richness in a given area. Although data from an area with many clam species may be as useful as data from a region with few species of different orders, distinguishing species with Raman spectroscopy may be possible only in the latter region. For our three regional training sets, only the training set from the Choptank River system was a true representation of all of the species known to spawn in that system in summer.

Classifying bivalve larvae with Raman spectroscopy Table 4 Classification accuracies when Bregional^ and a combination of Bregional^ and Btaxa-based^ training sets were used to identify spectra from samples which were obtained from different locations or years than those used in the training sets. The training sets are indicated by a letter and are followed by the samples that were tested with that training set. Information about each sample includes species, location, and year. The number of spectra in each sample can be cross-referenced in Table 1. The percentage of spectra within a sample that were correctly classified with a Test set/species Taxa-based Argopecten irradians A. irradians Crassostrea gigas Crassostrea virginica C. virginica Mercenaria mercenaria M. mercenaria M. mercenaria Mulinia lateralis Choptank River C. virginica C. virginica M. lateralis Northeast A. irradians A. irradians C. virginica C. virginica M. mercenaria M. mercenaria M. mercenaria West coast Crassostrea gigas Choptank taxa C. virginica C. virginica M. lateralis Northeast taxa A. irradians A. irradians C. virginica C. virginica M. mercenaria M. mercenaria M. mercenaria West coast taxa C. gigas

training set is reported and was calculated using a support vector machine. Separate tests were conducted using spectra taken at wavelengths of 532, 638, and 785 nm and using Bcombined^ spectra in which spectra that were taken at each wavelength were appended to produce one long spectrum. Two types of training sets were used: (1) the Bregional^ training sets (see Table 3) and (2) Btaxa-based^ training sets that included just the species found within each region. Bold numbers indicate the tests with the best accuracy for each sample

Location

Year

532 nm (%)

638 nm (%)

Dennis, MA Woods Hole, MA Hi lo, HI Dennis, MA Gloucester Point, VA Dennis, MA Dennis, MA Machias, ME Cambridge, MD

2008 2012 2012 2010 2009 2009 2010 2009 2009

41 98 74 82 83 100 100 100 66

68 75 48 41 62 95 100 100 57

6 20 76 66 82 32 9 9 40

57 84 31 41 30 100 100 100 89

Dennis, MA Gloucester Point, VA Cambridge, MD

2010 2009 2009

62 73 25

32 56 39

85 98 77

17 39 50

Dennis, MA Woods Hole, MA Dennis, MA Gloucester Point, VA Dennis, MA Dennis, MA Machias, ME

2008 2012 2010 2009 2009 2010 2009

73 91 70 85 77 98 85

57 77 45 71 86 100 100

23 36 62 92 14 84 77

61 70 48 71 77 93 92

Hi lo, HI

2012

98

100

71

98

Dennis, MA Gloucester Point, VA Cambridge, MD

2010 2009 2009

62 82 89

29 57 68

73 97 70

14 42 95

Dennis, MA Woods Hole, MA Dennis, MA Gloucester Point, VA Dennis, MA

2008 2012 2010 2009 2009

70 82 82 86 95

91 82 47 71 95

41 25 82 100 14

80 77 45 68 100

Dennis, MA Machias, ME

2010 2009

100 100

100 100

39 33

100 100

Hi lo, HI

2012

95

100

79

100

The performance of that training set and its subsequent classification of Bunknown^ groups may be the best indication of

785 nm (%)

Combine (%)

how Raman spectroscopy can be used to identify bivalve larvae in field applications.

C.M. Thompson et al.

Fig. 5 Decision tree for classifying bivalve larvae using visual inspection of spectra (i.e., without computer classification routines). The tree starts with 532 nm spectra and incorporates 638 and 785 nm for shells that do not show pigments and have flat or wavy spectra corresponding to thick

or thin shells, respectively. Wavenumbers from ν1 and ν2 peaks at 532 nm are used to differentiate spectra from shells that show pigment peaks

Overall, the Choptank River training set of eight species did not have greater than 80 % overall accuracy, but certain species had higher accuracies than others. For instance, the 785-nm wavelength had 77 % overall accuracy, but C. virginica, M. leucophaeata, G. demissa, I. recurvum, and R. cuneata all had greater than 80 % cross-validation accuracy, with C. virginica distinguished from the other species with 100 % accuracy (Table 3). When using this training set on the separate samples, C. virginica was distinguished with 85 and 95 % accuracy and M. lateralis with 77 % accuracy.

Therefore, this method may be able to successfully distinguish some species from the Choptank River based on the 785-nm spectra. Within-species differences in spectra were apparent when a training set (composed of spectra from one sample) were unable to successfully classify spectra of larvae in separate samples. For example, A. irradians from Dennis, MA was not successfully classified with the taxa-based and Northeast training sets (Table 4). Differences in Raman spectra of pigments within species could be a result of environmental conditions and the presence of available pigments for shell

Table 5 Results of decision-tree analysis for spectra from samples which were obtained from different locations or years than those used in training sets. Spectra from each sample were manually classified using the decision tree in Fig. 5. The number of spectra classified into each with pigment

taxonomic category and overall percent accuracy are reported for each sample. In some cases, classification was indeterminate for two categories and was reported for both categories (e.g., BSC/CLP^). KEY: OY oyster, OBNP other bivalve no pigment, MU mussel, SC scallop, CLP clams

Test set/species

Location

Year

OY

OBNP

MU

SC

CLP

SC/CLP

AI/MU

Total

Accuracy (%)

Argopecten irradians Argopecten irradians Crassostrea gigas Crassostrea virginica Crassostrea virginica Mercenaria mercenaria Mercenaria mercenaria Mercenaria mercenaria Mulinia lateralis

Dennis, MA Woods Hole, MA Hi lo, HI Dennis, MA Gloucester Point, VA Dennis, MA Dennis, MA Machias, ME Cambridge, MD

2008 2012 2012 2010 2009 2009 2010 2009 2009

0 0 62 49 62 0 0 0 0

0 0 4 17 4 1 0 0 0

0 0 0 0 0 0 0 0 0

35 60 0 0 0 3 0 0 1

9 0 0 0 0 37 22 44 64

0 0 0 0 0 3 0 0 1

0 6 0 0 0 0 0 0 0

44 66 66 66 66 44 22 44 66

80 91 94 74 94 84 100 100 97

Classifying bivalve larvae with Raman spectroscopy

incorporation [2] or to genetic differences that could also reflect adult coloration. A single adult shell can reveal multiple different pigment band shifts based on shell banding and coloration [4], and although larvae are not as variable in Raman measurements, the pigments present may eventually contribute to shell coloration. Also, given that some species do not show any pigment, it is questionable whether pigments are necessary components of the shell matrix. Comparisons of pigments in larval shells to pigments in the adult shells of the same species may shed light on the role of polyene pigments in bivalves. The fact that wavelengths that worked best in crossvalidation tests did not necessarily perform optimally when used to distinguish separate samples indicates that identification of species could be challenging when dealing with true unknowns and mixes of species as might occur in field samples. Slight differences in spectra that could be caused by genetic variation or growth conditions could result in misclassifications. Although we demonstrated that spectra of 14 species could be distinguished to order/family using Raman spectroscopy, further testing is needed before extrapolating this method to large groups of unknown species. Specifically, the analyses presented in this paper should be repeated at additional wavelengths and with more species that represent different orders and families that are not (relatively) easily obtainable from hatcheries. This could be facilitated by using a combination of genetic and Raman technologies to distinguish species by use of genetics and build a library of Raman spectra for each species. Chemotaxonomic methods using Raman spectroscopy to identify biological specimens have been explored with microbes, plants, and fungi. These methods employ characteristic signatures in Raman spectra, such as lipids, proteins, terpenes, and carotenoids [14, 16, 20]. These methods also employ cluster analysis either via Linear Discriminant Analysis, Neural Network or SVMs. Separation of fungal spores was found to be genus specific but not species specific [16]. In bacterial cells, high accuracy (>89 %) was observed for classification both to strain and species using single-bacterial cells in cultures. For bivalve larvae, Raman spectroscopy presents a nondestructive, relatively high-throughput method with a quick processing time that could complement or enhance established methods for larval identification. In particular, Raman spectroscopy coupled with the polarization feature on Raman microscopes could greatly augment image analysis methods for bivalve larval identification [11, 17] because it could be applied to help create and validate image training sets and be used in combination with birefringent images to enhance identification accuracies. Although more work is needed to apply this technology to identify larvae in systems with numerous species of bivalves, it can be applied now to distinguish species that have clear signatures which differ from other larvae

present a system (e.g., if one species of oyster is present in a system as C. virginica is in the Choptank River). In addition, decision-tree diagrams like that in Fig. 5 could be built for individual systems which would enable quick identification of target species and/or groups. Improvements in identification of bivalve larvae through application of this technology will lead to better understanding of the behavior, larval transport, and population connectivity of commercially and ecologically important species which in turn will enhance design of marine protected areas [21]. Acknowledgments Funding for this research was provided by the National Science Foundation (OCE-1240266, OCE-0829512). Many thanks to J. Goodwin, A. Schlenger, J. Spires, J. Thalmann, and T. Wazniak for help with specimen collection, spawning, and Raman analysis; A. Barton, S. Claussen, G. DeBrosse, J. Hetrick, D. McCorckle, and M. White for providing samples of larvae; and F. Adar at HORIBA Scientific, Inc. for invaluable advice on sample processing. This is UMCES contribution number 5003.

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